Description Usage Arguments Value Examples
Implements the k-nearest neighbors algorithm
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train_fgp |
a data frame containing the fingerprint vectors of the training set |
train_pos |
a data frame containing the positions of the training set observations |
k |
the k parameter for knn algorithm (number of nearest neighbors) |
method |
the method to compute the distance between the RSSI vectors: 'euclidean', 'manhattan', 'norm', 'LGD' or 'PLGD' |
weights |
the algorithm to compute the weights: 'distance' or 'uniform' |
norm |
parameter for the 'norm' method |
sd |
parameter for 'LGD' and 'PLGD' methods |
epsilon |
parameter for 'LGD' and 'PLGD' methods |
alpha |
parameter for 'PLGD' method |
threshold |
parameter for 'PLGD' method |
FUN |
an alternative function provided to compute the distance. This function must return a matrix of dimensions: nrow(test) x nrow(train), containing the distances from test observations to train observations. The two first parameters taken by the function must be train and test |
... |
additional parameters for provided function FUN |
An S3 object of class ipfModel, with the following properties: params -> a list with the parameters passed to the function data -> a list with the fingerprints and locations
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